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Scientific data2023; 10(1); 839; doi: 10.1038/s41597-023-02736-5

Gridded livestock density database and spatial trends for Kazakhstan.

Abstract: Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSK) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSK database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.
© 2023. The Author(s).
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Publication Date: 2023-11-29 PubMed ID: 38030700PubMed Central: PMC10687097DOI: 10.1038/s41597-023-02736-5Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This research developed a high-resolution (1 km) gridded database of livestock density in Kazakhstan from 2000 to 2019, focusing on horses, sheep, and goats.
  • The study identifies spatial patterns of livestock density and the factors influencing these patterns, providing valuable data for ecosystem management, disease control, and policy planning.

Introduction and Importance of Study

  • Livestock rearing is a vital source of livelihood in dryland regions of Asia, contributing to food security and income.
  • Increasing livestock densities impact ecosystem structure and function, exacerbate climate change effects, and help spread diseases.
  • Prior data on livestock density is limited and primarily available only at coarse administrative levels (e.g., county), lacking detailed spatial resolution.
  • High-resolution livestock distribution data is crucial for environmental monitoring, resource management, and policymaking.

Objectives

  • To create a high-resolution (~1 km grid) livestock density dataset covering Kazakhstan for the years 2000 to 2019.
  • To analyze spatial trends and identify livestock density hotspots across the country.
  • To investigate which environmental, socioeconomic, and physical factors contribute to the spatial distribution of livestock.

Methodology

  • Livestock data involved horses, sheep, and goats – key livestock species in Kazakhstan.
  • District-level livestock census data was the starting point for modeling spatial distribution.
  • Used a random forest (RF) regression model, a machine learning technique, to disaggregate district-level data into 1 km spatial grids.
  • Predictor variables included:
    • Vegetation proxies – indicators of forage availability and ecosystem characteristics.
    • Climatic variables – such as temperature, which influences livestock habitat suitability.
    • Socioeconomic variables – including population density, proximity to settlements, and nighttime lights as proxies for human activity.
    • Topographic variables – elevation and terrain features.
    • Proximity forcing variables – factors indicating accessibility and human-livestock interaction zones.
  • Combining these variables helped improve the accuracy of spatially distributing livestock numbers within districts.

Findings

  • High-density livestock areas were identified primarily in the south-central and southeastern regions of Kazakhstan.
  • Medium-density clusters appeared in northern and northwestern Kazakhstan.
  • Population density, distance to settlements, nighttime lighting data, and temperature were critical for effectively downscaling livestock counts to finer spatial resolution.
  • The spatial trends align with known livestock rearing practices and environmental suitability in Kazakhstan’s various regions.

Significance and Applications

  • The gridded livestock density database fills a significant data gap by providing detailed spatial livestock data for a major Eurasian country.
  • Researchers can use this dataset to better understand human-environment interactions, livestock impacts on ecosystems, and disease risk modeling.
  • Land managers and policymakers gain a tool for targeting sustainable livestock management and mitigating negative ecological impacts.
  • Potential to support climate adaptation strategies by correlating livestock density with environmental variables over time.
  • The high-resolution data aids regional and national planning strategies related to agriculture, health, and conservation.

Conclusion

  • This study demonstrates the feasibility and utility of employing machine learning and diverse data sources to produce high-resolution livestock density maps.
  • The resulting Kazakhstan livestock density database is a valuable resource for multiple stakeholders and contributes to filling a critical knowledge gap in dryland livestock management in Asia.

Cite This Article

APA
Kolluru V, John R, Saraf S, Chen J, Hankerson B, Robinson S, Kussainova M, Jain K. (2023). Gridded livestock density database and spatial trends for Kazakhstan. Sci Data, 10(1), 839. https://doi.org/10.1038/s41597-023-02736-5

Publication

Scientific data
ISSN: 2052-4463
NlmUniqueID: 101640192
Country: England
Language: English
Volume: 10
Issue: 1
Pages: 839
PII: 839

Researcher Affiliations

Kolluru, Venkatesh
  • Department of Sustainability and Environment, University of South Dakota, Vermillion, SD, 57069, USA. Venkatesh.Kolluru@coyotes.usd.edu.
John, Ranjeet
  • Department of Sustainability and Environment, University of South Dakota, Vermillion, SD, 57069, USA.
  • Department of Biology, University of South Dakota, Vermillion, SD, 57069, USA.
Saraf, Sakshi
  • Department of Biology, University of South Dakota, Vermillion, SD, 57069, USA.
Chen, Jiquan
  • Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, 48823, USA.
  • Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, 48823, USA.
Hankerson, Brett
  • Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120, Halle (Saale), Germany.
Robinson, Sarah
  • Institute for Agricultural Policy and Market Research & Centre for International Development and Environmental Research (ZEU), Justus Liebig University, Giessen, Germany.
Kussainova, Maira
  • Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, 48823, USA.
  • Kazakh National Agrarian Research University, AgriTech Hub KazNARU, 8 Abay Avenue, Almaty, 050010, Kazakhstan.
  • Kazakh-German University (DKU), Nazarbaev avenue, 173, 050010, Almaty, Kazakhstan.
Jain, Khushboo
  • Department of Sustainability and Environment, University of South Dakota, Vermillion, SD, 57069, USA.

MeSH Terms

  • Animals
  • Ecosystem
  • Goats
  • Horses
  • Kazakhstan
  • Livestock
  • Sheep

Conflict of Interest Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Citations

This article has been cited 4 times.
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